Fig.REF b (left) shows the performance of five-cycle models (15 layers, \(k=3\) ) on ImageNet.
CycleNet performs better than the baseline, but its accuracy is significantly lower when compared to MobileNet [1]}, which has a slightly larger size.
We experiment with larger models, and we followed two approaches to increase performance up to a comparable level.
First, similar to our CIFAR-10 experiment, we try a non-cubic architecture and find a \(10\%\)  gain in relative performance.
Second, following previous work [2]}, we hypothesise that image classification benefits from standard convolutions in the first layers, therefore we vary the number of initial cycles with standard convolutions.
Fig.REF b (right) shows that a model with two cycles of standard convolutions (2:3) has the best performance, and combining this with a non-cubic architecture, the model approaches the performance of MobileNet with a similar number of parameters.
This motivates using CycleNet and the CNN baseline for testing our working hypotheses in the next sections.
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